+41
-41
lines changedFilter options
+41
-41
lines changed Original file line number Diff line number Diff line change
@@ -50,9 +50,9 @@ def setup(self, unique, sort, dtype):
50 50
"float": pd.Index(np.random.randn(N), dtype="float64"),
51 51
"object_str": string_index,
52 52
"object": pd.Index(np.arange(N), dtype="object"),
53 -
"datetime64[ns]": pd.date_range("2011-01-01", freq="H", periods=N),
53 +
"datetime64[ns]": pd.date_range("2011-01-01", freq="h", periods=N),
54 54
"datetime64[ns, tz]": pd.date_range(
55 -
"2011-01-01", freq="H", periods=N, tz="Asia/Tokyo"
55 +
"2011-01-01", freq="h", periods=N, tz="Asia/Tokyo"
56 56
),
57 57
"Int64": pd.array(np.arange(N), dtype="Int64"),
58 58
"boolean": pd.array(np.random.randint(0, 2, N), dtype="boolean"),
@@ -93,9 +93,9 @@ def setup(self, unique, keep, dtype):
93 93
"uint": pd.Index(np.arange(N), dtype="uint64"),
94 94
"float": pd.Index(np.random.randn(N), dtype="float64"),
95 95
"string": tm.makeStringIndex(N),
96 -
"datetime64[ns]": pd.date_range("2011-01-01", freq="H", periods=N),
96 +
"datetime64[ns]": pd.date_range("2011-01-01", freq="h", periods=N),
97 97
"datetime64[ns, tz]": pd.date_range(
98 -
"2011-01-01", freq="H", periods=N, tz="Asia/Tokyo"
98 +
"2011-01-01", freq="h", periods=N, tz="Asia/Tokyo"
99 99
),
100 100
"timestamp[ms][pyarrow]": pd.Index(
101 101
np.arange(N), dtype=pd.ArrowDtype(pa.timestamp("ms"))
@@ -491,7 +491,7 @@ class BinaryOpsMultiIndex:
491 491
param_names = ["func"]
492 492 493 493
def setup(self, func):
494 -
array = date_range("20200101 00:00", "20200102 0:00", freq="S")
494 +
array = date_range("20200101 00:00", "20200102 0:00", freq="s")
495 495
level_0_names = [str(i) for i in range(30)]
496 496 497 497
index = pd.MultiIndex.from_product([level_0_names, array])
@@ -439,9 +439,9 @@ def setup(self, inplace, dtype):
439 439
N, M = 10000, 100
440 440
if dtype in ("datetime64[ns]", "datetime64[ns, tz]", "timedelta64[ns]"):
441 441
data = {
442 -
"datetime64[ns]": date_range("2011-01-01", freq="H", periods=N),
442 +
"datetime64[ns]": date_range("2011-01-01", freq="h", periods=N),
443 443
"datetime64[ns, tz]": date_range(
444 -
"2011-01-01", freq="H", periods=N, tz="Asia/Tokyo"
444 +
"2011-01-01", freq="h", periods=N, tz="Asia/Tokyo"
445 445
),
446 446
"timedelta64[ns]": timedelta_range(start="1 day", periods=N, freq="1D"),
447 447
}
@@ -649,7 +649,7 @@ def time_series_nunique_nan(self):
649 649
class Duplicated:
650 650
def setup(self):
651 651
n = 1 << 20
652 -
t = date_range("2015-01-01", freq="S", periods=(n // 64))
652 +
t = date_range("2015-01-01", freq="s", periods=(n // 64))
653 653
xs = np.random.randn(n // 64).round(2)
654 654
self.df = DataFrame(
655 655
{
@@ -212,7 +212,7 @@ def run(dti):
212 212
def time_datetime_to_period(self):
213 213
@test_parallel(num_threads=2)
214 214
def run(dti):
215 -
dti.to_period("S")
215 +
dti.to_period("s")
216 216 217 217
run(self.dti)
218 218
@@ -238,7 +238,7 @@ def time_series_nth(self, dtype):
238 238 239 239
class DateAttributes:
240 240
def setup(self):
241 -
rng = date_range("1/1/2000", "12/31/2005", freq="H")
241 +
rng = date_range("1/1/2000", "12/31/2005", freq="h")
242 242
self.year, self.month, self.day = rng.year, rng.month, rng.day
243 243
self.ts = Series(np.random.randn(len(rng)), index=rng)
244 244
@@ -232,7 +232,7 @@ def setup(self, index):
232 232
N = 100000
233 233
indexes = {
234 234
"int": Index(np.arange(N), dtype=np.int64),
235 -
"datetime": date_range("2011-01-01", freq="S", periods=N),
235 +
"datetime": date_range("2011-01-01", freq="s", periods=N),
236 236
}
237 237
index = indexes[index]
238 238
self.s = Series(np.random.rand(N), index=index)
@@ -465,7 +465,7 @@ def time_loc_row(self, unique_cols):
465 465
class AssignTimeseriesIndex:
466 466
def setup(self):
467 467
N = 100000
468 -
idx = date_range("1/1/2000", periods=N, freq="H")
468 +
idx = date_range("1/1/2000", periods=N, freq="h")
469 469
self.df = DataFrame(np.random.randn(N, 1), columns=["A"], index=idx)
470 470 471 471
def time_frame_assign_timeseries_index(self):
@@ -164,7 +164,7 @@ def time_unique_date_strings(self, cache, count):
164 164 165 165
class ToDatetimeISO8601:
166 166
def setup(self):
167 -
rng = date_range(start="1/1/2000", periods=20000, freq="H")
167 +
rng = date_range(start="1/1/2000", periods=20000, freq="h")
168 168
self.strings = rng.strftime("%Y-%m-%d %H:%M:%S").tolist()
169 169
self.strings_nosep = rng.strftime("%Y%m%d %H:%M:%S").tolist()
170 170
self.strings_tz_space = [
@@ -276,7 +276,7 @@ def time_dup_string_tzoffset_dates(self, cache):
276 276
# GH 43901
277 277
class ToDatetimeInferDatetimeFormat:
278 278
def setup(self):
279 -
rng = date_range(start="1/1/2000", periods=100000, freq="H")
279 +
rng = date_range(start="1/1/2000", periods=100000, freq="h")
280 280
self.strings = rng.strftime("%Y-%m-%d %H:%M:%S").tolist()
281 281 282 282
def time_infer_datetime_format(self):
@@ -89,7 +89,7 @@ class ToCSVDatetimeIndex(BaseIO):
89 89
fname = "__test__.csv"
90 90 91 91
def setup(self):
92 -
rng = date_range("2000", periods=100_000, freq="S")
92 +
rng = date_range("2000", periods=100_000, freq="s")
93 93
self.data = DataFrame({"a": 1}, index=rng)
94 94 95 95
def time_frame_date_formatting_index(self):
@@ -102,15 +102,15 @@ def time_frame_date_no_format_index(self):
102 102
class ToCSVPeriod(BaseIO):
103 103
fname = "__test__.csv"
104 104 105 -
params = ([1000, 10000], ["D", "H"])
105 +
params = ([1000, 10000], ["D", "h"])
106 106
param_names = ["nobs", "freq"]
107 107 108 108
def setup(self, nobs, freq):
109 109
rng = period_range(start="2000-01-01", periods=nobs, freq=freq)
110 110
self.data = DataFrame(rng)
111 111
if freq == "D":
112 112
self.default_fmt = "%Y-%m-%d"
113 -
elif freq == "H":
113 +
elif freq == "h":
114 114
self.default_fmt = "%Y-%m-%d %H:00"
115 115 116 116
def time_frame_period_formatting_default(self, nobs, freq):
@@ -130,15 +130,15 @@ def time_frame_period_formatting(self, nobs, freq):
130 130
class ToCSVPeriodIndex(BaseIO):
131 131
fname = "__test__.csv"
132 132 133 -
params = ([1000, 10000], ["D", "H"])
133 +
params = ([1000, 10000], ["D", "h"])
134 134
param_names = ["nobs", "freq"]
135 135 136 136
def setup(self, nobs, freq):
137 137
rng = period_range(start="2000-01-01", periods=nobs, freq=freq)
138 138
self.data = DataFrame({"a": 1}, index=rng)
139 139
if freq == "D":
140 140
self.default_fmt = "%Y-%m-%d"
141 -
elif freq == "H":
141 +
elif freq == "h":
142 142
self.default_fmt = "%Y-%m-%d %H:00"
143 143 144 144
def time_frame_period_formatting_index(self, nobs, freq):
@@ -253,7 +253,7 @@ class ReadCSVConcatDatetime(StringIORewind):
253 253
iso8601 = "%Y-%m-%d %H:%M:%S"
254 254 255 255
def setup(self):
256 -
rng = date_range("1/1/2000", periods=50000, freq="S")
256 +
rng = date_range("1/1/2000", periods=50000, freq="s")
257 257
self.StringIO_input = StringIO("\n".join(rng.strftime(self.iso8601).tolist()))
258 258 259 259
def time_read_csv(self):
@@ -25,7 +25,7 @@ def _generate_dataframe():
25 25
df = DataFrame(
26 26
np.random.randn(N, C),
27 27
columns=[f"float{i}" for i in range(C)],
28 -
index=date_range("20000101", periods=N, freq="H"),
28 +
index=date_range("20000101", periods=N, freq="h"),
29 29
)
30 30
df["object"] = tm.makeStringIndex(N)
31 31
return df
@@ -122,7 +122,7 @@ def setup(self, format):
122 122
self.df = DataFrame(
123 123
np.random.randn(N, C),
124 124
columns=[f"float{i}" for i in range(C)],
125 -
index=date_range("20000101", periods=N, freq="H"),
125 +
index=date_range("20000101", periods=N, freq="h"),
126 126
)
127 127
self.df["object"] = tm.makeStringIndex(N)
128 128
self.df.to_hdf(self.fname, "df", format=format)
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